Classifications

• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q10/00—Administration; Management
  • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
  • G06Q10/063—Operations research, analysis or management
  • G06Q10/0637—Strategic management or analysis, e.g. setting a goal or target of an organisation; Planning actions based on goals; Analysis or evaluation of effectiveness of goals
  • G06Q10/06375—Prediction of business process outcome or impact based on a proposed change
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/02—Marketing; Price estimation or determination; Fundraising
  • G06Q30/0201—Market modelling; Market analysis; Collecting market data
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/02—Marketing; Price estimation or determination; Fundraising
  • G06Q30/0201—Market modelling; Market analysis; Collecting market data
  • G06Q30/0202—Market predictions or forecasting for commercial activities
• • G—PHYSICS
  • G06—COMPUTING; CALCULATING OR COUNTING
  • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
  • G06Q30/00—Commerce
  • G06Q30/02—Marketing; Price estimation or determination; Fundraising
  • G06Q30/0201—Market modelling; Market analysis; Collecting market data
  • G06Q30/0206—Price or cost determination based on market factors

Definitions

• the present invention relates generally to modeling demand of a good and, more particularly, to systems, methods and computer program products for modeling demand, supply and associated profitability of a good based on variable market size models, prices paid by consumers and costs.
• Demand refers to the quantity of a good that is demanded by consumers at any given price.
• demand is often illustrated by a downward-sloping curve on a graph of price versus quantity for a specified time period.
• supply and demand curves can be useful tools that can aid in modeling the profitability of a given good.
• demand curves that are used by manufacturers are based on historical data and estimates. But because adequate historical data and estimates are often lacking in a large number of industries, demand curves are rarely used to model profitability for given goods. Also aiding in the lack of use, demand curves often inadequately integrate variable factors such as market size and prices paid by consumers for given goods. Further, demand curves generally do not account for the impact of variability in the relationship of prices to the number of a given good sold.
• the present invention provides systems, methods and computer program products for modeling demand, supply and associated profitability of a good.
• the systems, methods and computer program products of the present invention advantageously are capable of modeling the demand, supply and associated profitability based on sparse historical data or estimates regarding present and future price and quantity of the good.
• the present invention is capable of modeling the demand, supply and, thus the profitability as a function of the size of the market within which the good is sold more adequately than conventional methods of modeling the demand.
• the systems, methods and computer program products model the demand, supply and associated profitability while better accounting for variability in the relationship of the price of the good and the number of units of the good purchased.
• embodiments of the present invention are capable of modeling demand, supply and associated profitability while accounting for uncertainty in the price of the good and the number of units of the good purchased.
• Such modeling is advantageous in a number of different contexts, such as in the context of commercial transactions.
• programs for the future sale of goods inherently have associated uncertainty, particularly as it relates to the market for the goods, typically defined by the number of good purchased and the price at which each unit of the good is purchased.
• demand, supply and associated profitability of a good can be modeled in a manner that facilitates deriving an understanding about a future market that is uncertain, particularly when data regarding price and number of units of the good purchased are sparse.
• a method is provided that includes modeling demand and/or for a good based upon a price sensitivity distribution of a unit purchase of the good and a market potential distribution of a number of units of the good in a market. According to the method, the price sensitivity distribution of a unit purchase of the good can be determined.
• the price sensitivity distribution can be determined by determining a price sensitivity distribution of a unit purchase at a predetermined price, and thereafter recasting the price sensitivity distribution in a reverse cumulative format.
• the price sensitivity distribution can be determined by determining a price sensitivity distribution of a unit purchase at a predetermined price, and thereafter recasting the price sensitivity distribution in a cumulative format.
• the price sensitivity distribution can comprise a lognormal price sensitivity distribution and can be determined based upon a mean price at which the unit is purchased and an associated standard deviation.
• the market potential distribution can similarly comprise a lognormal market potential distribution and can be determined based upon a mean number of units at which the good is demanded and/or supplied in the market and an associated standard deviation.
• Both price sensitivity and market potential can be represented by distributions other than lognormal distributions in order to best represent economic market realities or data limitations.
• the price sensitivity and market potential distributions may more accurately represents how changing the price of the good changes whether consumers will purchase the good, and whether manufacturers will produce the good.
• a market Before modeling demand and/or supply, a market can be forecasted by randomly selecting a predefined number of units of the good based upon the market potential distribution, such as according to a Monte Carlo method.
• the method is capable of modeling demand and/or supply, and thus the profitability, as a function of the size of the market within which the good is sold more reliably than conventional methods of modeling demand or supply. And after selecting the forecasted market, a market penetration distribution can be determined based upon different numbers of units of the good that represent corresponding percentages of the predefined number of units in the forecasted market.
• demand and/or supply for the good in the forecasted market can be modeled based upon the distribution of a unit purchase and the predefined number of units in the forecasted market. For example, demand and/or supply can be modeled by determining a relationship between a plurality of prices per unit and different numbers of units of the good that represent corresponding percentages of the predefined number of units in the forecasted market. In embodiments including the market penetration distribution, demand and/or supply can be modeled based upon the price sensitivity distribution and the market penetration distribution. In another embodiment, different forecasted markets are repeatedly selected, with each forecasted market including a predetermined number of units of the good.
• the method further includes determining at least one contract purchases collection for at least one predefined number of contracts, where each contract includes a number of units of the good at a predetermined price per unit.
• the contract purchases collections can be determined based upon a units per contract distribution and a correlation between the distribution of a unit purchase and the units per contract distribution.
• the contract purchases collections can be determined by determining the number of units included in each of a predefined number of contracts based upon a units per contract distribution, such as according to the Monte Carlo method.
• a number of units in each contract are determined such that the sum of the number of units included in each contract equals a percentage of the predetermined number of units in the forecasted market, where the percentage is representative of the market share.
• a price per unit for the units included in each of the predefined number of contracts can be determined based upon the number of units included in each contract and a correlation between the distribution of consumers purchasing a unit and the units per contract distribution.
• demand and/or supply for the good can be modeled based upon the contract purchases collections and the market potential distribution or, more particularly, a forecasted market selected from the market potential distribution.
• the demand can be modeled by calculating a cumulative number of units for each different price per unit determined by summing the number of units in each contract having a price per unit no less than the respective price per unit, and thereafter modeling the demand for the good based upon each different price per unit and the respective cumulative number of units.
• the supply can be modeled by calculating a cumulative number of units for each different price per unit determined by summing the number of units in each contract having a price per unit no greater than the respective price per unit, and thereafter modeling the supply for the good based upon each different price per unit and the respective cumulative number of units.
• the cumulative number of units can comprise the difference between the predefined number of units in the forecasted market and the sum of number of units in each contract having a price per unit no greater than the respective price per unit.
• the cumulative number of units can comprise the difference between the predefined number of units in the forecasted market and the sum of number of units in each contract having a price per unit no less than the respective price per unit.
• the method can include modeling cost of the good along with the demand. Then, profitability of the good can be modeled based upon the cost model and the demand model.
• the cost can be modeled by modeling an average cost per unit for the different numbers of units. Then, for example, the profitability can be modeled by subtracting the average cost per unit for the different numbers of units from the prices per unit for the respective different numbers of units. A maximum profit can be determined for a certain number of units and price correspondence.
• the demand, cost and profitability can be modeled for each forecasted market.
• cost for the good in the forecasted market can be modeled based upon the predefined number of contracts and the predefined number of units in a forecasted market.
• the cost can be modeled by modeling an average cost per unit for each cumulative number of units associated with the different prices per unit and thereafter selecting the lowest cost per unit.
• the profitability of the good in the forecasted market can then be modeled, for example, by subtracting the lowest cost per unit from the price per unit associated with the each cumulative number of units.
• the systems, methods and computer program products of the present invention therefore are capable of facilitating an understanding about demand and/or supply for a good in an uncertain future market, where demand and/or supply can be defined based upon the number of units of a good purchased for a price per unit.
• the systems, methods and computer program products are capable of modeling the demand, supply and associated profitability based on sparse historical data or estimates regarding price and quantity of the good. Also, by incorporating the unknown size of the market in the form of the market potential distribution, and by selecting a forecasted market according to the Monte Carlo method ⁇ the present invention is capable of modeling the demand, supply and, thus the profitability, as a function of the size of the market within which the good is sold more adequately than conventional methods of modeling the demand and supply. Additionally, by including a lognormal price sensitivity distribution, the systems, methods and computer program products model demand, supply and associated profitability while better accounting for how changing the price of the good changes the number of units of the good purchased.
• FIG. 1 is a flowchart including various steps in a system, method and computer program product for modeling the demand for a good purchased in a non- differentiated market according to one embodiment of the present invention
• FIG. 2 is a graph of a price sensitivity distribution utilized during operation of the system, method and computer program product of one embodiment of the present invention
• FIG. 3 is a graph of a market potential distribution utilized during operation of the system, method and computer program product of one embodiment of the present invention
• FIG. 4A is a schematic illustration of a price sensitivity distribution recast in reverse cumulative format utilized during operation of the system, method and computer program product for modeling demand according to one embodiment of the present invention
• FIG. 4B is a schematic illustration of a price sensitivity distribution recast in cumulative format utilized during operation of the system, method and computer program product for modeling supply according to one embodiment of the present invention
• FIG. 5 is a schematic illustration of a market penetration distribution for a forecasted market utilized during operation of the system, method and computer program product of one embodiment of the present invention
• FIG. 6A is a schematic illustration of a demand curve for a forecasted market according ⁇ to one embodiment of the present invention in the context of a good purchased in a non-differentiated market;
• FIG. 6B is a schematic illustration of a supply curve for a forecasted market according to one embodiment of the present invention in the context of a good purchased in a non-differentiated market;
• FIG. 7 is a schematic illustration of multiple demand curves for multiple forecasted markets according to one embodiment of the present invention in the context of a good purchased;
• FIG. 8 is a flowchart including various steps in a system, method and computer program product for modeling the demand for a good purchased in a differentiated market according to one embodiment of the present invention;
• FIG. 9 is a graph of a units per contract distribution utilized during operation of the system, method and computer program product of one embodiment of the present invention;
• FIG. 10 is a is a graph illustrating a contract purchases collection utilized during operation of the system, method and computer program product of one embodiment of the present invention.
• FIG. 11 is a schematic illustration of a demand curve for a forecasted market according to one embodiment of the present invention in the context of a good purchased in a differentiated market
• FIG. 12 is a schematic illustration of a cost curve utilized during operation of the system, method and computer program product of one aspect of the present invention in the context of a good purchased in a non-differentiated market
• FIG. 13 is a schematic illustration comparing the demand curve of FIG. 6 with the cost curve of FIG. 12;
• FIG. 14 is a schematic illustration of a profitability curve according to one embodiment of the present invention.
• FIG. 15 is a schematic illustration of a price sensitivity distribution plotted against a distribution of optimum price and an associated cost distribution
• FIG. 16 is a schematic illustration of the market value of a project of selling a good over time determined according to one embodiment of the present invention.
• FIG. 17 is a schematic illustration of a cost curve utilized during operation of the system, method and computer program product of one embodiment of the present invention in the context of a good purchased in a differentiated market;
• FIG. 18 is a schematic block diagram of the system of one embodiment of the present invention embodied by a computer.
• products can generally be categorized in one of two markets, either a non-differentiated market or a differentiated market.
• a non-differentiated market such as a commodity market
• all competing goods are offered at a single price.
• wheat, cotton, silver and oil are all goods that are typically offered at a single price.
• the prices of competing goods can be differentiated by the perceived values in the features that characterize the respective goods.
• the value of the distinguishing features are revealed and determined during private negotiations between a supplier and a consumer. As the negotiation results of price remain private, the process allows different amounts of goods to be sold at different prices. For example, wholesaled lots of automobiles and aircraft are both goods that, due to differing features, can be sold at differing prices for differing quantities.
• Another type of differentiated market is one where there are sophisticated pricing systems that exploit small differences among buyer preferences, for example airplane seat, travel and concert seat reservation systems, and department store merchandise pricing coupled with membership discounting.
• a non-differentiated market all goods are typically sold and purchased according to a single price for each unit of the good.
• the goods can vary in price.
• goods in a differentiated market are typically sold according to contracts for a predetermined number of units of the good at a predetermined price for each unit.
• the present invention provides systems, methods and computer program products for modeling demand and/or for goods in a non-differentiated market as well as a differentiated market. The following description will explain modeling demand for goods. In this regard, it will be appreciated that, except were indicated, the following description applies equally to modeling supply for goods.
• the demand for a good in a non-differentiated market is generally a function of the price per unit of the good and the size of the market in terms of the total number of units of the good in the market, both of which differ depending upon the good.
• neither the price of the good nor the size of the market can be specified as each includes an amount of uncertainty.
• the demand is typically modeled based upon a distribution of the possible prices for which the good may be sold, and a distribution of the possible sizes of the market within which the good may be sold.
• modeling the demand for a good in a non-differentiated market generally begins by assessing uncertainty in the price per unit of the good by determining how the price of the good affects whether customers will purchase the good, or in the case of modeling supply, how the price of the good affects whether manufacturers will produce the good.
• uncertainty in the purchase price of each unit of the good is typically expressed in a price sensitivity distribution of a unit purchase of the good at a predetermined price, as shown in block 10.
• the price sensitivity distribution generally assigns a probability of a unit purchase to each respective price at which consumers would purchase the unit.
• the price sensitivity distribution can be developed from sparse data of real or hypothetical consumer purchases of at least one unit of the good at respective prices per unit developed according to any one of a number of different methods, such from a number of historical sales or a market survey.
• the price sensitivity distribution can be developed with as little as two such consumer purchases, however, the number of purchases typically numbers thirty or more.
• uncertainty in the price of the good, or the price sensitivity distribution can be defined based upon a state of development of technologies associated with the good.
• technologies associated with the good in many industries, decisions about projects for the manufacture and sale of a good require manufacturers to estimate technical risk, or technical maturity, associated with the state of development of the project in order to correctly determine success probabilities and investment levels for the project, i.e., to determine risk and return probabilities.
• development of the project can include one or more different technologies, with different technologies in different stages of development.
• TTL's Technology Readiness Levels
• NSA National Aeronautics and Space Administration
• each technology associated with the good is associated with a qualitative measure of maturity, where each qualitative measure of maturity is associated with a distribution.
• each technology is associated with the distribution of the respective qualitative measure of maturity.
• a price point or more typically a most likely price, is selected for each technology.
• a price distribution can then be defined for each technology based upon the distribution associated with the respective qualitative measure of maturity and the respective most likely price.
• a price for each technology can be selected from the price distributions, such as according to the Monte Carlo method, and thereafter summed together to get one possible total price for the good.
• a number of other prices for each technology can be selected and summed together in a similar manner to get a number of other possible total prices. From all of the total prices, a mean and standard deviation can be determined to thereby define the price sensitivity distribution. For more information on such a method of determining the price sensitivity distribution, see U.S. Patent Application No. , entitled: Systems, Methods and Computer Program Products for
• the price sensitivity distribution can be expressed according to any of a number of different probability distribution types such as normal, triangular or uniform. But because the economy typically functions in a lognormal fashion, in a preferred embodiment the price sensitivity distribution is expressed as a lognormal probability distribution. Also, the price sensitivity distribution can be defined according to any of a number of different parameters, such as the mean and standard deviation of the historical sales. For example, the price sensitivity distribution shown in FIG. 2 is defined according to a mean of $92,252 (in thousands of dollars) with an associated standard deviation of $7,800.
• the demand can advantageously be modeled as a function of the size of the market within which the good is purchased to thereby account for uncertainty in the size of the market.
• uncertainty in the size of the market is typically represented as a market potential that refers to the total number of units of the good consumers will purchase presuming all consumer requirements are met, including price, as shown in block 12 of FIG. 1.
• the market potential is typically expressed as a distribution of consumers purchasing a predetermined number of units of the good.
• the market potential distribution generally assigns a probability to each respective number of units of the good consumers will purchase presuming all consumer requirements are met.
• the market potential distribution can also be developed from sparse data from any one of a number of different sources, such as market studies or a myriad of other factors as known to those skilled in the art.
• the market potential distribution can be expressed according to any of a number of different probability distribution types such as normal, triangular or uniform but, like the market sensitivity distribution, the market potential distribution is preferably expressed as a lognormal probability distribution.
• the market potential distribution can be defined according to any of a number of different parameters, such as the mean and standard deviation of the data used to develop the market potential distribution. For example, the market potential distribution shown in FIG. 3 is defined according to a mean of 700 units with an associated standard deviation of 400 units.
• the demand for the good is modeled as a function of the size of the market within which the good is sold.
• a forecasted market of a predefined total number of units of the good is selected from the market potential distribution.
• the number of units in the forecasted market is selected according to a method for randomly selecting a predefined number of units of the good, such as the Monte Carlo method.
• the Monte Carlo method is a method of randomly generating values for uncertain variables to simulate a model.
• the Monte Carlo method is applied to the market potential distribution to select the predefined number of units of the good in the forecasted market, as shown in block 14 of FIG. 1.
• a corresponding demand for the good can be modeled for each forecasted future market to thereby facilitate an understanding of how different market sizes affect demand for the good.
• a market penetration distribution can be determined based upon different numbers of units that represent corresponding percentages of the forecasted market, as shown in block 16. For example, as shown in FIG. 5, in a market size of 700 units of the good, a sale of 350 units would be associated with a market penetration of 50%. In an alternative market penetration distribution, represented as a dotted line in FIG. 5, in a market size of 700 units of the good, a sale of 200 units would be associated with a market penetration of 50%.
• the demand can be modeled based upon the price sensitivity distribution and the market penetration distribution.
• the price sensitivity distribution is typically first recast in reverse cumulative format, as shown in FIG. 4A. (See FIG. 1, block 18).
• a reverse cumulative distribution depicts the number, proportion or percentage of values greater than or equal to a given value.
• the reverse cumulative of the price sensitivity distribution represents the distribution of a unit purchase of the good for at least a predetermined price, i.e., at or above a predetermined price.
• the price sensitivity distribution is typically first recast in cumulative format, as shown in FIG. 4B.
• a cumulative distribution depicts the number, proportion or percentage of values less than or equal to a given value.
• the cumulative of the price sensitivity distribution represents the distribution of a unit manufactured when the market price for the good is at least a predetermined price, i.e., at or above a predetermined price.
• the demand for the product for the forecasted market can be modeled based upon the reverse cumulative of the price sensitivity distribution and the market penetration distribution, as shown in block 20 of FIG. 1.
• the demand represents the number of units consumers will purchase for at least a given price, i.e., at or above a given price.
• each probability percent of the reverse cumulative of the price sensitivity distribution is associated with a corresponding percentage of the forecasted market from the market penetration distribution.
• each of a plurality of different numbers of units of the good from the market penetration distribution are linked to a minimum price per unit from the reverse cumulative price sensitivity distribution having a probability percent equal to the market penetration percent for the respective number of units.
• the demand model can be thought of as a plurality of different numbers of units sold in the forecasted market, each number of units having a corresponding minimum price at which consumers will purchase the respective number of units. For example, a number of goods totaling 700 and having a market penetration of 100% is linked to a price per unit of approximately $77 million dollars having a probability percent of 100%. Thus, according to the demand model, 700 units of the good will be sold for at least $77 million dollars.
• the demand model can be represented in any one of a number of manners but, in one embodiment, the demand model is represented as a demand curve by plotting different numbers of units sold in the forecasted market versus the minimum price consumers will pay per unit for the good, as shown in FIG. 6.
• the supply for the product for the forecasted market can be modeled based upon the cumulative of the price sensitivity distribution and the market penetration distribution.
• the supply represents the number of units manufacturers will produce when the market price for the good is at least a given price, i.e., at or above a given price.
• each probability percent of the cumulative of the price sensitivity distribution is associated with a corresponding percentage of the forecasted market from the market penetration distribution.
• each of a plurality of different numbers of units of the good from the market penetration distribution are linked to a maximum price per unit from the cumulative price sensitivity distribution having a probability percent equal to the market penetration percent for the respective number of units.
• the supply model can be thought of as a plurality of different numbers of units produced in the forecasted market, each number of units having a corresponding maximum market price.
• the supply model can be represented in any one of a number of manners. In one embodiment, for example, the supply model is represented as a supply curve by plotting different numbers of units produced in the forecasted market versus the maximum market price of the good, as shown in FIG. 6B.
• the demand for the good is based upon the reverse cumulative of the price sensitivity distribution and the market penetration distribution
• the supply for the good is based upon the cumulative of the price sensitivity distribution and the market distribution.
• the steps in determining the reverse cumulative (or cumulative) of the price sensitivity distribution and the market penetration distribution can be accomplished in any order relative to one another without departing from the spirit and scope of the present invention.
• the price sensitivity distribution can be rewritten in reverse cumulative format before any or all of the steps in determining the market penetration distribution from the market potential distribution.
• the demand model can account for the uncertainty in the size of the market affecting the demand for the good.
• the demand for the good in each forecasted market can be modeled stochastically.
• modeling the demand for the good can be utilized with a cost model to model profitability for the good in the forecasted market which, in turn, can be used to determine conclusions regarding the forecasted market, such as the optimum price per unit and the number of units sold.
• the profitability can be modeled for each forecasted market, and the conclusions can be determined for each forecasted market.
• the conclusions for the forecasted markets can then be used, such as by the manufacturer, to facilitate an understanding of how uncertainty in the price of the good and number of units in the market affect demand for the good. With such an understanding, the manufacturer can be in a better position to select a price at which to sell each unit of the good, as well as a number of units of the good to produce.
• the demand is preferably modeled based upon a distribution of the possible prices for which the good may be sold, and a distribution of the possible sizes of the market within which the good may be sold and/or produced.
• Goods in differentiated markets differ from those in non- differentiated markets, however, in that the prices per unit of the goods are not uniform across the market. In this regard, prices per unit of the goods can be uniform within each of the plurality of contracts that include the units of the good that make up the market.
• prices per unit of the goods can be uniform within a given number of goods of the contract, such as a contract that includes 1-100 units of the good for $75M, 101-200 units for $70M, 201-300 units for $65M, etc.
• a contract that includes 1-100 units of the good for $75M, 101-200 units for $70M, 201-300 units for $65M, etc.
• consideration is advantageously given to the number of units of the good in each contract.
• the number of units per contract is preferably utilized in conjunction with the other distributions. Referring now to FIG.
• modeling the demand for a good in a differentiated market generally begins the same as modeling the demand in a non-differentiated market, that is by assessing uncertainty in the price per unit of the good by determining how the price of the good affects whether customers will purchase the good.
• modeling supply in a non-differentiated market generally begins by determining how the price of the good affects whether manufacturers will produce the good.
• the price sensitivity of the good is typically expressed as before with the price sensitivity distribution, as shown in block 22 and FIG. 2.
• the market potential can likewise be expressed as before by a market potential distribution of consumers purchasing a predetermined number of units of the good, as shown in block 24 of FIG. 8 and FIG. 3.
• the demand is modeled as a function of the size of the market within which the goods are sold to thereby account for uncertainty in the size of the market.
• the predefined number of units of the good in the forecasted market is selected from the market potential distribution according to the Monte Carlo method, as shown in block 26.
• a corresponding demand for the good purchased in a differentiated market can be modeled for each forecasted market.
• non-differentiated markets differ from differentiated markets in that goods in non-differentiated markets are all sold and purchased for a uniform price, as opposed to differing prices based on individual units.
• the goods are sold according to contracts that each specify a predetermined number of units of the good at a predetermined price for each unit.
• modeling the demand for the good further includes assessing the uncertainty in the number of contracts in the market, as well as uncertainty in the predetermined number of units of the good in each contract and the predetermined price per unit at which each unit in each contract is purchased.
• uncertainty in the number of units in each contract can be assessed by determining a units per contract distribution, shown in FIG. 9 and in block 28 of FIG. 8.
• the units per contract distribution is typically expressed as a distribution of units of the good included in each contract.
• the units per contract distribution generally assigns a probability to each respective number of units that may be included in a particular contract.
• the units per contract distribution can be developed from sparse historical data, such as a number of historical contracts including a number of units of the good.
• the units per contract distribution can be developed with as little as two historical contracts, however, the number of historical contracts typically numbers thirty or more.
• the units per contract distribution can be expressed according to any of a number of different probability distribution types such as normal, triangular or uniform but, as before, in a preferred embodiment the units per contract distribution is expressed as a lognormal probability distribution. Also, the units per contract distribution can be defined according to any of a number of different parameters, such as the 10% and 90% values, as such are known. Further, the units per contract distribution can include a maximum value that sets the upper bound of the distribution. For example, the price sensitivity distribution shown in FIG. 9 is defined according to a 10% value of 2 units per contract, a 90% value of 40 units per contract and a maximum value of 70 units per contract.
• a contract purchases collection can be determined to include a number of contracts each having a number of units of the good and an associated price per unit.
• the forecasted market can be selected, such as according to the Monte Carlo method, so that the total number of units in all of the contracts included within the contract purchases collection can be based upon the forecasted market. Presuming a total capture of the forecasted market by the manufacturer (i.e., selling all of the units in the entire market), the total number of units in all of the contracts can then be set equal to the number of units in the forecasted market.
• the total number of units in all of the contracts can be set equal to a percentage of the number of units in the forecasted market.
• the method of the present invention described below refers to the forecasted market, it should be understood that in instances where less than a total capture of the forecasted market is presumed, the presumed capture of the forecasted market will preferably be utilized in place of the number of units in the forecasted market.
• a relationship between the price sensitivity distribution and the units per contract distribution is typically first established (see FIG. 8, block 30), such as via a correlation coefficient, as is known to those skilled in the art.
• the correlation coefficient can be selected in any one of a number of manners, however, the correlation coefficient is typically a non-positive number such that as the price per unit in a given contract increases, the number of units in the contract decreases, and vice versa.
• the con-elation coefficient is determined in accordance with conventional techniques based upon a number of historical contractual sales of the good or a similar good, where each sale includes a number of units of the good at a price per unit.
• the contract purchases collection can be determined by first determining the number of units in each contract, as shown in block 32.
• a predefined number of contracts and the number of units in each contract are preferably determined according to the Monte Carlo method based upon the units per contract distribution. Because the forecasted market has been defined to include a predefined number of units of the good in the market, the aggregate number of units in each contract within the forecasted market totals the predefined number of units in the forecasted market or, alternatively, a percentage of the predefined number of units if less than total market capture of the forecasted market is presumed.
• the Monte Carlo method can be used to repeatedly select different numbers of contracts and different numbers of units in each contract, so long as the aggregate number of units in each contract within the forecasted market does not exceed the predefined number of units in the forecasted market (or percentage of the predefined number). By repeatedly selecting different numbers of contracts and different numbers of units in each contract, many different contract purchases collections can be determined for the forecasted market.
• the associated price per unit of the units in each contract is determined based upon the number of units in the respective contract, the price sensitivity distribution and the correlation between the units per contract distribution and the price sensitivity distribution, as shown in block 34.
• a contract purchases collection for the forecasted market can be determined as a plurality of contracts, with each contract having an associated number of units of the good at a given price per unit, as shown in block 36.
• the contract purchases collection can be represented in any one of a number of manners but, in one embodiment, the contract purchases collection is represented as a scatter plot of the units in each contract at the corresponding price per unit, as shown in FIG. 10 with a forecasted market of 681 units and a presumed market capture of 60% (i.e., 409 units).
• the contract purchases collection can be determined by determining a correlation between the price sensitivity distribution and the units per contract distribution, selecting a number of contracts and a number of units in each contract according to the Monte Carlo method, and thereafter determining a price per unit contract. It will be understood, however, that the contract purchases collection can be determined in any of a number of different manners. For example, the contract purchases collection can be determined by determining the correlation and thereafter selecting a number of contracts, such as randomly selecting a defined number of contracts (e.g., 100 contracts). With the number of contracts, then, a price sensitivity distribution and a units per contract distribution can be defined for each contract, where the distributions can differ between one or more contracts or remain the same across all of the defined number of contracts. Where the distributions differ between one or more contracts, the correlation can similarly differ but, when the distributions remain the same across all of the contracts, the correlation is preferably the same across all of the contracts.
• a number of units in the respective contract can be determined, such as from the units per contract distribution according to the Monte Carlo method.
• an associated price per unit for each of the defined contracts can be determined based upon the number of units, in the respective contract, the respective price sensitivity distribution and the co ⁇ elation between the units per contract distribution and the price sensitivity distribution.
• the contract purchases collection can be determined as a plurality of contracts, with each contract having an associated number of units of the good at a given price per unit.
• the demand for the good in the forecasted market represents the number of units consumers may purchase for at least a given price.
• the price per unit of each contract can be ranked in descending order from the highest price per unit down.
• a cumulative number of units for each different price per unit can then be calculated, as shown in block 38 of FIG. 8.
• the cumulative number of units for each price then would equal the cumulative number units across all of the contracts sold for a price per unit equal to or greater than the respective price.
• the cumulative number of units associated with the highest price per unit would equal the number of units in each contact having the highest price per unit.
• the cumulative number of units associated with the second highest price per unit would equal the number of units in each contract having the second highest price per unit plus the number of units in each contact having the highest price per unit.
• the supply for the good in the forecasted market represents the number of units manufacturers may produce for no more than a given i market price.
• the price per unit of each contract can be ranked in ascending order from the lowest price per unit up.
• a cumulative number of units for each different price per unit can then be calculated as the cumulative number units across all of the contracts sold for a price per unit less than or equal to the respective price. For example, the cumulative number of units associated with the lowest price per unit would equal the number of units in each contact having the lowest price per unit. Then, the cumulative number of units associated with the second lowest price per unit would equal the number of units in each contract having the second lowest price per unit plus the number of units in each contact having the lowest price per unit.
• the price per unit of each contract can equally be ranked in ascending order from the lowest price per unit up.
• the cumulative number of units for each price would equal the total number of units in the forecasted market minus the number of units in each contract with a price per unit lower than the respective price.
• the cumulative number of units associated with the lowest price per unit would equal the number of units in the forecasted market or, alternatively, the percentage of the forecasted market.
• the cumulative number of units associated with the second lowest price per unit would then equal the number of units in the forecasted market minus the number of units in each contract with the lowest price per unit.
• the price per unit of each contract can equally be ranked in descending order from the highest price per unit up when modeling supply.
• the cumulative number of units for each price would equal the total number of units in the forecasted market minus the number of units in each contract with a price per unit higher than the respective price.
• the cumulative number of units associated with the highest price per unit would equal the number of units in the forecasted market or, alternatively, the percentage of the forecasted market.
• the cumulative number of units associated with the second highest price per unit would then equal the number of units in the forecasted market minus the number of units in each contract with the highest price per unit.
• the demand for the good in the forecasted market can be modeled based upon the price per unit of each of the contracts and the cumulative number of units sold for a price per unit equal to or greater than the respective price per unit, as shown in block 40 of FIG. 8.
• the demand model for a good in a non-differentiated market represents the number of units consumers may purchase for at least a given price.
• the demand model can be thought of as a plurality of different numbers of units sold in the forecasted market, each number of units having a co ⁇ esponding minimum price at which consumers will purchase the respective number of units.
• the supply model for a good in a differentiated market represents the number of units manufacturers may produce when the market price is no more than a given price.
• the supply model can be thought of as a plurality of different numbers of units produced in the forecasted market, each number of units having a co ⁇ esponding maximum market price for the good.
• the demand model can be represented in any one of a number of manners but, like in the case of the model for the non-differentiated market, in one embodiment the demand model is represented as a demand curve by plotting the different prices per unit versus the cumulative number of units sold for a price per unit equal to or greater than the respective price per unit, as shown in FIG. 11 with a forecasted market of 681 units and a presumed market capture of 409 units.
• the supply model can be represented in any one of a number of manners but, in one embodiment, the supply model is represented as a supply curve by plotting the different prices per unit versus the cumulative number of units produced when the good has a market price per unit less than or equal to the respective price per unit. As illustrated in FIG.
• the demand and supply models typically do not appear as smooth as the demand and supply models in the case of the non-differentiated market.
• the coarseness of the demand and supply models for the differentiated market is due to the fact that the model uses distinct contractual sales, as opposed to considering the entire non-differentiated market as one contractual sale. It will be appreciated that as the total number of units in the forecasted market changes according to the Monte Carlo method for the demand model for either the case of the non-differentiated market or the differentiated market, the demand model changes to fit the total number of units of the good.
• modeling the demand for the good can be utilized with a cost model to model the profitability of the good in the forecasted market which, in turn, can be used to determine conclusions regarding the forecasted market, such as the optimum price per unit and the number of units sold. And by repeating the method for different forecasted markets, the profitability can be modeled for each forecasted market, and the conclusions can be determined for each forecasted market.
• the conclusions for the forecasted markets can then be used, such as by the manufacturer, to facilitate an understanding of how uncertainty in the price of the good, the number of units and/or contracts, as well as the price per unit of the good in the contracts, affects demand for the good. With such an understanding, then, the manufacturer can be in a better position to select a price at which to sell each unit of the good and a number of units of the good to produce.
• the profitability of the good can be modeled thereby facilitating an understanding of how uncertainty in demand for the good, as well as uncertainty in cost of producing the good, can affect profitability.
• the demand model differs depending on whether the goods are in a non-differentiated market or a differentiated market
• the profitability of the good also differs depending on the type of market.
• the present invention provides systems, methods and computer program products for modeling the profitability of a good for goods in both non- differeiitiated markets as well as differentiated markets.
• Modeling the profitability of a good in a non-differentiated market generally begins by modeling the demand for the good, such as according to embodiments of the present invention as described above with reference to FIGS. 1-6.
• the cost of producing the good is also modeled.
• the cost model is typically based on the average cost per unit to produce the good and the number of units produced, or sold.
• the cost model accounts for uncertainty in the size of the market, just as does the demand model.
• the cost of producing the good can be modeled in any one of a number of manners, the cost preferably considers the effect of the number of units produced, or sold, on the cost to produce each unit of the good. In this regard, costs associated with producing a good in many markets tend to decline as the manufacturer gains experience with that production.
• the cost to produce each unit of the good is typically more than the expected cost of producing each unit for the first units produced. And as the number of units produced increases, the manufacturer typically gains experience that drives the cost to produce each unit down to the expected cost and below, and thereafter eventually leveling to an optimum cost of producing each unit.
• the change in the cost to produce each unit can generally be considered to be attributable to a "learning curve" experienced by the manufacturer in manufacturing the good.
• a cost model accounting for a learning curve can be represented in any one of a number of different manners but, in one embodiment, the cost model is represented as a reverse cumulative cost curve by plotting the different costs per unit versus the cumulative number of units produced for the respective cost per unit, as shown in FIG. 12.
• the cost to produce each unit of a good in a non- differentiated market can be modeled, see U.S. Patent Application No. , entitled: Systems, Methods and Computer Program Products for Determining A Learning Curve Value and Modeling Associated Profitability and Costs of A Good, filed concu ⁇ ently herewith, the contents of which are hereby incorporated by reference in its entirety.
• the profitability for the good for the forecasted market can be modeled.
• the profitability can be represented as the result of subtracting the cost per unit from the price per unit and multiplying the difference by the number of units sold for the co ⁇ esponding fraction of the forecasted market.
• FIG. 13 by simultaneously plotting the demand curve and the cost curve for the forecasted market, the profitability can be seen as directly related to the distance between the two curves.
• the profitability model can be represented in any one of a number of different manners. In one embodiment, shown in FIG.
• the profitability model can be represented as a profitability curve by plotting the number of units that must be sold to achieve at least a given profit. From the profitability model, as well as the demand and cost models, conclusions regarding the forecasted market can be drawn from collectively modeling the demand, cost and profitability for the forecasted market. For example, the maximum profit for the good in the forecasted market can be seen as the point where the price exceeds the cost by the greatest amount. By determining the maximum profit, the optimum price for each unit of the good and the optimum number of units sold in the forecasted market (i.e., fraction of the number of goods in the market), as well as the co ⁇ esponding cost associated with the optimum price and number of units sold, in the forecasted market can be determined.
• the maximum profit margin for the forecasted market can be determined by dividing the difference between the optimum price and associated cost by the optimum price, and thereafter recorded. Further, the price per unit and number of units at which the forecasted market clears can be determined from the point where the profitability is zero (or the point where the demand model intersects the cost model).
• the demand and the cost models, as well as the profitability model, up to this point have all been tied to one forecasted market of a predefined number of goods selected according to a method for randomly selecting a predefined number of units of the good, such as the Monte Carlo method.
• the conclusions can be recorded, and thereafter the method can then be repeated a plurality of times for different forecasted markets selected according to the Monte Carlo method, with the conclusions recorded for each forecasted market.
• the conclusions for all of the forecasted markets can also be organized into respective distributions.
• the distributions can then be defined, such as by a curve type and a mean and associated standard deviation.
• Various of the conclusions and other variables can then be plotted against one another, such as by plotting the price sensitivity distribution against distributions for the optimum price and recurring cost, as shown in FIG. 15. It will be noted that the right tail of the recurring cost distribution is truncated from the plot. In such instances, recurring costs exceeded price so the manufacturer realizes no gross profits, and as such, the manufacturer in such instances is likely to terminate sale of the good by the principals of real options. Only data on successful business case 'instances' are tabulated, resulting in statistical information (mean, most likely, standard deviation, etc.) that is indicative of target prices or costs required to be successful in the market modeled.
• a business case for the good can be created.
• the business case can receive the distribution for the maximum profit (e.g., gross profit), as such may be determined based upon the optimum price for each unit and the co ⁇ esponding optimum number of goods.
• the market value of the project can be determined and plotted over time, as shown in FIG. 16.
• the business case can plot the nonrecurring costs associated with the project (shown below zero for years three through five).
• the business case can plot the profit associated with the project, as determined by the difference between gross profits and recurring costs (shown above zero for years six through fourteen).
• the market value, as well as the nonrecurring costs can be plotted over time according to any of a number of different techniques.
• the market value and nonrecurring costs are plotted over time based upon a measure of uncertainty in the risk and return associated with the good, as well as how the uncertainty can vary over time.
• modeling the profitability also generally begins by modeling the demand for a number of contracts for the good including the number of units and associated price per unit.
• the demand for the good can advantageously be modeled according to the present invention as described above with reference to FIGS. 2, 3 and 8-11.
• the cost of producing the good can be modeled based on the average cost per unit to produce the good and the number of units produced, or sold.
• the cost of producing the good can be modeled in any one of a number of manners, such as according to the method described above.
• the learning curve used to model the cost is a function of the cumulative number of units associated with each price per unit, as described above in conjunction with modeling the demand for goods in a differentiated market. As shown in FIG. 17, just as the demand for the differentiated market appears coarse as a plurality of connected contractual sales, the cost curve likewise appears as a plurality of connected costs for producing respective cumulative numbers of units for each price per unit.
• the profitability can be represented in a manner similar to the non-differentiated market. That is, the profitability can be represented for each contract as the difference of the respective price per unit and the respective cost per unit multiplied by the number of units sold for the respective contract.
• the demand model for the good in the context of a differentiated market describes individual contractual sales
• the cost model describes average cost and is based on a number of units sold
• a number of units sold must be selected in order to model the profitability of the good for the forecasted market. If the number of contracts or the number of units in one or more contracts changes, or if the number of units in the presumed percentage capture of the market changes, the average cost of producing the units for each contract would likewise change, thus changing the model of the profitability.
• the cost model can be replaced with the lowest cost value for the respective forecasted market (shown by the dashed line on FIG. 17), or for percent capture of the forecasted market.
• the cost model can be so replaced since the lowest cost value always co ⁇ esponds to capturing the market share of the forecasted market and, thus, selling all of the units of the good the manufacturer produces. Profitability, then, can be measured by the profitability of the forecasted market (presuming total market capture) based upon the profitability of each contractual sale within the forecasted market.
• the profitability of each contractual sale can be determined by subtracting the lowest average cost to produce the number of units in the contract from the price per unit of the units in the contract, and multiplying the difference by the number of units in the contract.
• the profitability of the forecasted market can then be modeled by determining a cumulative profitability at each contractual sale.
• the profitability of the forecasted market for each price per unit can be determined by adding the profit for the contractual sale having the respective price per unit with the profit for the other contractual sales with units making up the cumulative number of units associated with the price per unit, as defined in conjunction with modeling the demand, i.e., the profit from contractual sales having the same or greater prices per unit than the respective price per unit.
• the profitability of the forecasted market at the highest price per unit equals only profit from the contractual sales having the highest price per unit.
• the profitability at the highest price per unit equals such because the cumulative number of units associated with the highest price per unit only includes the number of units in the contracts having the highest price per unit.
• the profitability at the second highest price per unit equals the profit from the contractual sales having the second highest price per unit plus the profit from the contractual sales having the highest price per unit.
• the cumulative units associated with the second highest price per unit equals the number of units in each contract having the second highest price per unit plus the number of units in each contact having the highest price per unit.
• the profitability model for the forecasted market can be misleading because the profitability model can appear as though increasing the number of units in the forecasted market would better maximize profits.
• the misconception is caused by the fact that, by increasing the predefined number of units in the forecasted market or presumed capture of the forecasted market, the lowest average cost to produce the units decreases.
• one manufacturer of the good can only capture a defined share of the market.
• increasing the number of units produced does not necessarily increase the share of the market captured by the respective manufacturer.
• producing more units of the good does not necessarily mean that the manufacturer of the good will be able to sell the additional units above the market share of the respective manufacturer.
• the forecasted market can be drawn from collectively modeling the demand, cost (or lowest cost value) and profitability for the forecasted market. For example, because the maximum profit corresponds to selling as many units as possible, the maximum profit for the good in the forecasted market (or in the percent capture of the forecasted market) can be seen as the point where all of the units of the good in either the market, or percent capture of the market, have been sold. Also, for example, a price to achieve maximum profits can be determined, such as by determining a weighted average price per unit from all of the contractual sales in the forecasted market (or captured percentage).
• the demand and the cost, as well as the profitability of the good for differentiated markets up to this point have all been tied to a forecasted market of a predefined number of goods selected according to the Monte Carlo method.
• the conclusions can be recorded.
• the method can then be repeated a plurality of times for different forecasted markets selected according to the Monte Carlo method, with the conclusions recorded for each forecasted market.
• the conclusions for all of the forecasted markets can then be organized into respective distributions.
• the distributions can then be defined, such as by a curve type and a mean and associated standard deviation.
• a business case for the good can be created, such as in a manner similar to that shown in FIG. 16 and described in U.S. Patent Application No. , entitled: Systems, Methods and Computer Program Products for Modeling Uncertain Future Benefits.
• the profitability model actually demonstrates a negative profitability, or a loss for sales of the good.
• Contingent claims oftentimes come in the form of a call in which the manufacturer has an option to invest an amount of money, or additional amounts of money, in order to start producing or continue producing the good.
• the manufacturer will likely decline to invest the money, or additional money, and thereby forego exercise of the call and will therefore decline to produce the good or terminate production of the good.
• the manufacturer will likely make the necessary investment in order to begin or continue production of the good. Regardless of the type of contingent claim, it is desirable to determine the value of a good and, in particular, the contingent claim at the present time.
• the manufacturer can avoid overpaying for production of the good as a result of an overvaluation of the contingent claim.
• the manufacturer can identify goods in which the value of the contingent claim has been undervalued and can give strong consideration to investing in the production of these goods since they likely represent worthwhile investment opportunities.
• the systems, methods and computer program products of the present invention can facilitate determining the value of the good and, in particular, the contingent claim at the present time.
• the systems, methods and computer program products of the present invention therefore are capable of modeling the demand, supply and associated profitability based on sparse historical data or estimates regarding price and quantity of the good.
• the present invention is capable of modeling the demand, supply and, thus the profitability as a function of the size of the market within which the good is sold more adequately than conventional methods of modeling the demand.
• the present invention is capable of modeling the demand, supply and associated profitability while better accounting for how changing the price of the good changes the number of units of the good purchased.
• embodiments of the present invention are capable of modeling demand, supply and associated profitability to thereby facilitate an understanding of how uncertainty in a market affects demand, supply and profitability.
• such an understanding can be advantageous to those associated with the manufacture, sale and purchase of the good, such as in the context of commercial transactions.
• programs for the future sale of goods inherently have associated uncertainty, particularly as it relates to the market for the goods, typically defined by the number of good purchased and the price at which each unit of the good is purchased.
• demand, supply and associated profitability of a good can be modeled in a manner such that a manufacturer can be in a better position to not only decide whether to bring a good to market, but to also select a price at which to sell each unit of the good, as well as a number of units of the good to produce.
• the system of the present invention is typically embodied by a processing element and an associated memory device, both of which are commonly comprised by a computer 40 or the like.
• the method of embodiments of the present invention can be performed by the processing element manipulating data stored by the memory device with any one of a number of commercially available computer software programs.
• the method can be performed with data that is capable of being manipulated and/or presented in spreadsheet form.
• the method can be performed by the processing element manipulating data stored by the memory device with Excel, a spreadsheet software program distributed by the Microsoft Corporation of Redmond, Washington, including Crystal Ball, a Monte Carlo simulation software program distributed by Decisioneering, Inc. of Denver, Colorado.
• the computer can include a display 42 for presenting information relative to performing embodiments of the method of the present invention, including the various distributions, models and/or conclusions as determined according to embodiments of the present invention.
• the computer can further include a printer 44.
• the computer 40 can include a means for locally or remotely transferring the information relative to performing embodiments of the method of the present invention.
• the computer can include a facsimile machine 46 for transmitting information to other facsimile machines, computers or the like.
• the computer can include a modem 48 to transfer information to other computers or the like.
• the computer can include an interface (not shown) to a network, such as a local area network (LAN), and/or a wide area network (WAN).
• a network such as a local area network (LAN), and/or a wide area network (WAN).
• the computer can include an Ethernet Personal Computer Memory Card International Association (PCMCIA) card configured to transmit and receive information to and from a LAN, WAN or the like.
• PCMCIA Personal Computer Memory Card International Association
• the methods according to embodiments of the present invention may be embodied in a software or data module, component, portfolio or the like, that can be manipulated or otherwise operated within a spreadsheet software program such as Excel.
• a technique may be advantageous in a number of different contexts, such as in the context of financial modeling and analysis.
• modules, components and/or a portfolio that perform various financial modeling functions can be combined to gain a more complete understanding of a financial context.
• data capable of being manipulated to perform at least a portion of the methods of the present invention can be embodied in a module, which can thereafter be linked or otherwise associated with other portions of the methods of the present invention embodied in other modules so as to formulate a component. Then, if so desired, the component can be linked or otherwise associated with other components capable of performing other related methods to thereby form a portfolio.
• methods of modeling demand according to embodiments of the present invention can be embodied in one module while methods of modeling cost according to embodiments of the present invention can be embodied in another module. The two modules can then be linked or otherwise associated with one another to formulate a component capable of modeling profitability based upon the demand and cost models.
• the component for modeling profitability can be linked or otherwise associated with another component to perform another function.
• the component for modeling profitability can be linked or otherwise associated with a component capable of forecasting revenue over time to thereby create a business case for the good.
• a component capable of forecasting revenue over time may operate according to U.S. Patent Application No. , entitled: Systems, Methods and Computer
• the system of the present invention generally operates under control of a computer program product.
• the computer program product for performing the methods of embodiments of the present invention includes a computer-readable storage medium, such as the non-volatile storage medium, and computer-readable program code portions, such as a series of computer instructions, embodied in the computer-readable storage medium.
• FIGS. 1 and 7 are flowcharts of methods, systems and program products according to the invention. It will be understood that each block or step of the flowchart, and combinations of blocks in the flowchart, can be implemented by computer program instructions. These computer program instructions may be loaded onto a computer or other programmable apparatus to produce a machine, such that the instructions which execute on the computer or other programmable apparatus create means for implementing the functions specified in the flowchart block(s) or step(s).
• These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart block(s) or step(s).
• the computer program instructions may also be loaded onto a computer or other programmable apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block(s) or step(s).
• blocks or steps of the flowchart support combinations of means for performing the specified functions, combinations of steps for performing the specified functions and program instruction means for performing the specified functions. It will also be understood that each block or step of the flowchart, and combinations of blocks or steps in the flowchart, can be implemented by special purpose hardware-based computer systems which perform the specified functions or steps, or combinations of special purpose hardware and computer instructions.

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